Discussion of Identifying Neighborhood Effects Among Firms: - PowerPoint PPT Presentation

Discussion of “ Identifying Neighborhood Effects Among Firms: Evidence from Location Lotteries of the Tokyop Tsukiji Fish Market ” by Kentaro Nakajima and Kensuke Teshima Jessie Handbury, Wharton CJEB Japan Economic Seminar, February 16, 2018

Store Placement • Top three in mall sales: 1. Women’s clothing (16.6%) 2. Shoes (9.9%) 3. Food (9.8%) • Where would you place these stores in this mall? 2

Summary Question: Do stores agglomerate for the sake of demand spillovers? • – Old theory behind the spatial organization of retail – Why? Search costs (macro), trip chaining (micro) Identification Challenges: • – Selection of firms into high-demand locations Solution : relocation lotteries in wholesale fish markets – Production externalities Solution : differentiated effects from front-facing vs. back-facing neighbors Clean identification of firm-level benefits of demand spillovers • – Jardim (2016) identifies intra-firm spillovers separately from location attributes – Relihan (2017) measures demand spillovers directly with shopping data

Results Small, specialized stores benefit from demand spillovers • – One additional fish specialty neighbor increases the probability of expansion by 25% from the baseline (9%) – One standard deviation increase in diversity increases probability of expansion by 45%, equivalent to effect of being a (highly visible) corner store Zero evidence of supply spillovers (even within-group) • No positive demand spillovers for multi-shop retailers •

where: • – Δ y igr = change in the number of stores applied for between 1990 and 1995 • exit igr if firm exits between 1990 and 1995 lotteries • Δ Shops igr difference in number of shops applied for, conditional on survival • I[ Δ Shops igr >0] dummy for whether the number of shops increased between the 1990 and 1995 applications – Diversity r = number of trade groups represented in the neighborhood or HHI within neighborhood as the result of 1990 lottery Dynamics: • – Shop’s position between 1990 and 1995 determines its expected sales in 1995 lottery position. – Either buyers find them and stick with them or they use the increased revenues in 1990 position to invest in technology.

where: • – Δ y igr = change in the number of stores applied for between 1990 and 1995 • exit igr if firm exits between 1990 and 1995 lotteries • Δ Shops igr difference in number of shops applied for, conditional on survival • I[ Δ Shops igr >0] dummy for whether the number of shops increased between the 1990 and 1995 applications – Diversity r = number of trade groups represented in the neighborhood or HHI within neighborhood as the result of 1990 lottery Space: • – “Neighborhood” = area separated by larger corridors – Implicit assumption: effects operate within but not across regions – Implication: corner (“border”) stores are less valuable, not more – Why not use a broader neighborhood definition to account for wider effects?

Economic Significance Trade-off between clean identification and generalizability • – Finding for corroborating evidence for null results (lack of supply-side spillovers) in the broader Japanese retail market might be tough. – Start by looking at another fish market with different allocation mechanism? – Instead motivate with spatial distribution of Japanese retail in Census data? Do the results suggest an efficient spatial organization for the market? • – e.g., tuna stores = anchor for sushi fish wholesalers How much do stores value demand externalities? • – Can pre-trading secondary market be used to measure the role of consumption externalities in firm agglomeration? – Is there sorting into group applications (self-selected horizontal neighbors)? – Do other fish markets auction spaces?

8 Source: WSJ, November 24, 2015